Previous studies on visual effects primarily focus on evaluating the overall urban environment, lacking specific research on historical districts within cities. In order to evaluate the visual effects of plantscapes in historic districts, street view images and machine learning methods were used. The ResNeSt model was selected to assess the coordination and health of plantscapes. The results show that the ResNeSt model performs best in classification and regression tasks. Its scores are consistent with expert evaluations and moderately to highly correlated with public evaluations. Additionally, the visual effects of plantscapes are significantly influenced by economic factors, with the visual effect scores of streets outside the historic districts generally higher than those inside. It is concluded that machine learning models are highly effective in evaluating the visual effects of plantscapes in historic districts. This provides a scientific basis for their protection and optimization, with important implications for urban planning and tourism.